Investment-less Growth: An Empirical Investigation

Transcription

1 Investment-less Growth: An Empirical Investigation Germán Gutiérrez and Thomas Philippon November 2016 Abstract We analyze private fixed investment in the U.S. over the past 30years. Weshowthatinvestment is weak relative to measures of profitability and valuation particularly Tobin s Q, and that this weakness starts in the early 2000 s. There are two broad categories of explanations: theories that predict low investment because of low Q, and theories that predict low investment despite high Q. We argue that the data does not support the first category, and we focus on the second one. We use industry-level and firm-level data to test whether under-investment relative to Q is driven by (i) financial frictions, (ii) measurement error (due to the rise of intangibles, globalization, etc), (iii) decreased competition (due to technology or regulation), or (iv) tightened governance and/or increased short-termism. We do not find support for theories based on risk premia, financial constraints, or safe asset scarcity, and only weak support for regulatory constraints. Globalization and intangibles explain some of thetrendsattheindustrylevel,but their explanatory power is quantitatively limited. support for the competition and short-termism/governance hypotheses. On the other hand, we find fairly strong Industries with less entry and more concentration invest less, even after controlling for current market conditions. Within each industry-year, the investment gap is driven by firms that are owned by quasiindexers and located in industries with less entry/more concentration. disproportionate amount of free cash flows buying back their shares. These firms spend a We are grateful to Janice Eberly, Olivier Blanchard, René Stulz, Boyan Jovanovic, Tano Santos, Charles Calomiris, Glenn Hubbard, Holger Mueller, Alexis Savov, Philipp Schnabl, Ralph Koijen, Ricardo Caballero, Emmanuel Farhi and seminar participants at Columbia University and New York University for stimulating discussions. New York University New York University, CEPR and NBER 1

2 In his March 2016 letter to the executives of S&P 500 firms, BlackRock s CEO Laurence Fink argues that, in the wake of the financial crisis, many companies have shied away from investing in the future growth of their companies. Too many companies have cut capital expenditure and even increased debt to boost dividends and increase share buybacks. The decline in investment has been discussed in policy papers [Furman, 2015], especially in the context of a perceived decrease in competition in the goods market [CEA, 2016]. There is little systematic evidence, however, on the extent of the investment puzzle and on the potential explanations. This paper tries to (at least partially) fill that gap. We clarify some of the theory and the empirical evidence; and test whether alternate theories of under-investment are supported by the data. The main contributions of the paper are to show that: (i) thelackofinvestmentrepresents areluctancetoinvestdespitehightobin sq; and(ii)thisinvestmentwedgeappearstobelinked to decreased competition and changes in governance that encourage shares buyback instead of investment. We address the issues of causality of competition and governance in a companion paper [Gutiérrez and Philippon, 2016]. It is useful, as a starting point, to distinguish two broad categories of explanations for low investment rates: theories that predict low investment because they predict low Tobin s Q and theories that predict low investment despite high Tobin s Q. Thefirstcategoryincludestheoriesof increased risk aversion or decreases in expected growth. The standardq-equation holds in these theories, so the only way they can explain low investment is by predictinglowvaluesofq. The second category ranges from credit constraints to oligopolistic competition, and predicts agap between Q and investment due to differences between average and marginal Q (e.g., market power, growth options) and/or differences between firm value and the manager s objective function (e.g., governance, short-termism). We find that private fixed investment is weak relative to measures of profitability and valuation particularlytobin sq. Time effects from industry- and firm-level panel regressionsonqare substantially lower since This is true controlling for firmage,size,andprofitability;focusing on subsets of industries; and even considering tangible and intangible investment separately. Given these results, we discard theories that predict low investment because they predict low Q. We therefore focus on theories that predict agapbetween Q and investment; and we consider the following eight potential explanations, grouped into four broad categories. See Section 2 for a detailed discussion of these hypotheses. Financial frictions 1. External finance 2. Bank dependence 3. Safe asset scarcity Measurement Error 4. Intangibles 2

3 5. Globalization Competition 6. Regulation 7. Concentration due to other factors Governance 8. Ownership and Shareholder Activism We emphasize that these hypotheses are not mutually exclusive. For instance, there is a large and growing literature that focuses precisely on the interaction between governance and competition (see, for example, Giroud and Mueller [2010, 2011]). Thus, our tests do not map one-to-one into hypotheses (1) to (8); some tests overlap two or more hypotheses (e.g., measures of firm ownership affect both short-termism and governance). We report the results of our tests and discuss their implications for the above hypotheses in Section 4. Testing these hypotheses requires a lot of data, at different levels of aggregation. Some are industry-level theories (e.g., competition), some firm-level theories (e.g., ownership), and some theories that can be tested at the industry level and/or at the firm level. Unfortunately,firm-and industry-data are not readily comparable, because they differ in their definitions of investment and capital, and in their coverage. As a result, we must spend a fair amount of time simply reconciling the various data sources. Much of the work is explained in Section 3 and in the Appendix. We gather industry investment data from the BEA and firm investment data from Compustat; as well as additional data needed to test each of the eight hypotheses. For instance, for Entry and Concentration, we obtain measures of firm entry, firm exit, and concentration(salesandmarket value Herfindahls, and concentration ratios, i.e., the share ofsalesandmarketvalueofthetop 4, 8, 20 and 50 firms in each industry). For governance and short-termism, we use measures of institutional ownership, including different ownership types following Brian Bushee s institutional investor classification. 1 Competition and Governance We then analyze investment patterns at the industry- and firmlevel. At the industry level, we find that industries with more quasi-indexer institutional ownership and less competition (as measured by changes in the number of firms, as well as sales and market value concentration) invest less. These results are robust to controlling for firm demographics (age and size) as well as Q. Thedecreaseincompetitionissupportedbyagrowingliterature 2,thoughthe 1 The classification described in Bushee [2001] identifiesquasi-indexer,transientanddedicatedinstitutional investors based on the turnover and diversification of their holdings. Dedicated institutions have large, long-term holdings in a small number of firms. Quasi-indexers have diversified holdings and low portfolio turnover consistent with a passive, buy-and-hold strategy of investing portfolio funds in a broad set of firms. Transient owners have high diversification and high portfolio turnover. See Section 3 for additional details. 2 For instance, the Council of Economic Advisers issued a 2016 issue brief that reviews three sets of trends that are broadly suggestive of a decline in competition: increasing industry concentration, increasing rents accruing to a few firms, and lower levels of firm entry and labor market mobility. (see also Decker et al. [2015]). 3

4 empirical implications for investment have not been recently studied (to our knowledge). Similarly, the mechanisms through which quasi-indexer institutional ownership impacts investment remain to be fully understood: while such ownership may improve governance (e.g., Appel et al. [2016a]), it may also increase short-termism (e.g., Asker et al. [2014], Bushee [1998]) both of which could lead to higher buybacks and less investment. Industries with a higher share of intangibles exhibit lower investment and we find some weak evidence that industries with moreregulationalsoinvestless. Industries with higher foreign profits invest less in the US, as expected, but firm level investment does not depend on the share of foreign profits. Firm-level results are consistent with industry-level results. They suggest that within each industry-year and controlling for Q, firmswithhigherquasi-indexerinstitutionalownershipinvest less; and firms in industries with less competition also invest less. None of the other theories appear to be supported by the data, and they often exhibit the wrong and/orinconsistentsigns;orare not statistically significant. Safe Assets and Intangibles To better understand the implications of safe asset scarcityandthe rise of intangibles, we discuss these hypotheses in greater detail. According to the safe asset scarcity hypothesis, the value of being able to issue safe assets has increased after the Great Recession. In that case, the valuation (and investment) of highly rated firms should increase relative to that of other firms. We regress the 2014 value on the 2006 value and an indicator for AA to AAA rated firms and find no support for the hypothesis. We also fail to observe higher investment for these firms in the cross-section. The rise of intangibles may affect investment in several ways: first, intangibleinvestmentis difficult to measure and is therefore prone to measurement error. Under-estimation of I would lead to under-estimation of K, andthereforeover-estimationofq; andwouldtranslatetoan observed under-investment at industries with a higher share of intangibles. Alternatively, intangible assets might be more difficult to accumulate. A rise in the relative importance of intangible could then lead to a higher equilibrium value of Q even if intangibles are correctly measured. We find some support for these hypotheses but their impact does not seem to bequantitativelyverylarge. Other Papers Overall, our results are aligned with Lee et al. [2016] whofindthatindustriesthat receive more funds have a higher industry Q until the mid-1990s, but not since then. The change in the allocation of capital is explained by a decrease in capital expenditures and an increase in stock repurchases by firms in high Q industries since the mid-1990s. Our results are also related to Alexander and Eberly [2016] whostudytheimplicationsoftheriseofintangiblesoninvestment. Last, our results somewhat contrast with Bena et al. [2016], who study the relationship between foreign institutional ownership (proxied by additions to the MSCI World Index), investment and innovation across 30 countries. They find that foreign institutional ownership can increase long-term investment in fixed capital, innovation, and human capital. It will therefore be interesting, in future work, to understand if our results are specific to the United States. Finally, the above conclusions 4

5 Table 1: Current Account of Non financial Sector Value in 2014 ($ billions) Name Notation Corporate 1 Non corporate 2 Business 1+2 Gross Value Added P ty t $8,641 $3,147 $11,788 Net Fixed Capital at Rep. Cost Pt k Kt $14,857 $6,126 $20,983 Consumption of Fixed Capital δ tpt k Kt $1,286 $297 $1,583 Net Operating Surplus P ty t W tn t T y t δtp t k Kt $1,614 $1,697 $3,311 Gross Fixed Capital Formation Pt k It $1,610 $354 $1,964 Net Fixed Capital Formation Pt k (It δtkt) $325 $56 $381 are based on simple regressions and therefore cannot establish causality between competition, governance and investment. In follow-up work [Gutiérrez and Philippon, 2016] weuseacombinationof instrumental variables and natural experiments to test the causality of our two main explanations, lack of competition and tight or short-termist governance. The remainder of this paper is organized as follows. Section 1 presents five important facts about aggregate private fixed investment in recent years. Section 2 discusses the theories that may explain under-investment relative to Q and reviews the related literature. Section 3 describes the data used to test our eight hypotheses. Section 4 discusses the methodology and results of our analyses; and section 5 concludes. 1 Five Facts about US Non Financial Sector Investment We present five important facts related to investment by the USnonfinancialsectorinrecentyears. We focus on the non financial sector for three main reasons. First, this sector is the main source of nonresidential investment. Second, we can roughly reconcile aggregate data from the Flow of Funds with industry-level investment data from the BEA (which includes residential and non residential investment, as well as investment in intellectual property). Last, we can use data on the market value of bonds and stocks for the non financial corporate sector to disentangle various theories of secular stagnation. 1.1 Fact 1: The Non financial Business Sector is Profitable but does not Invest Table 1 summarizes some key facts about the balance sheet and current accountofthenonfinancial corporate, non financial non corporate and non financial business sectors. One reason investment might be low is that profits might be low. This,however,isnotthecase. Figure 1 shows the operating return on capital of the non financial corporate, non financial non corporate and non financial business sector, defined as net operating surplus over the replacement cost of capital: Net Operating Return = P ty t δ t Pt kk t W t N t T y t Pt kk t 5

6 As shown, the operating return for corporates has been quite stable over time while the operating return of non corporates has increased substantially since For corporates, the yearly average from 1971 to 2014 is 10%, with a standard deviation of only one percentage point. The minimum is 8.1% and the maximum 12.6%. In 2014, the operating return was 11.3%, very close to the historical maximum. For non corporates, the yearly average from 1971 to 2014 is 24%, while the average since 2002 has been 27%. The maximum is 28.9%, equal to the operating returnobservedin2012,2013 and A striking feature is that the net operating margin was not severely affected by the Great Recession, and has been consistently near its highest value since 2010 for both Corporates and Non corporates. Figure 1: Net Operating Return, by Sector year Non Financial Corporate Non Financial Business Non Financial Non Corporate Note: Annual data, by Non financial Business sector. But firms do not invest the same fraction of their operating returns as they used to. Figure 2 shows the ratio of net investment to net operating surplus for thenonfinancialbusinesssector: NI/OS = Pt k (I t δ t K t ) P t Y t δ t Pt kk t W t N t T y t The average of the ratio between 1959 and 2001 is 20%. The average of the ratio from 2002 to 2015 is only 10%. 3 Current investment is low relative to operating margins. Similar patterns are observed when separating corporates and non corporates. 3 Note that 2002 is used for illustration purposes only. It was chosen based on graphically, not based on a formal statistical analysis. 6

7 Figure 2: Net Investment Relative to Net Operating Surplus year Note: Annual data for Non financial Businesses (Corporate and Noncorporate). 1.2 Fact 2: Investment is low relative to Q Of course, economic theory does not say that NI /OS should be constant over time. Investment should depend on expected future operating surplus, on the capital stock, and the cost of funding new investment; it should rely on a comparison of expected returns on capital and funding costs. The Q-theory of investment captures this trade-off. Consider a firm that chooses a sequence of investment to maximize its value. Let K t be capital available for production at the beginning of period t and let µ t be the profit margin of the firm. The basic theory assumes perfect competition so the firm takes µ as given. In equilibrium, µ depends on productivity and production costs (wages, etc.). The firm s program is then V t (K t )=maxµ t P t K t Pt k I t γ ( ) 2 I t 2 P t k It K t δ t + E t [Λ t+1v t+1 (K t+1)], K t where Pt k is the price of investment goods. Given our homogeneity assumptions, it is easy to see that the value function is homogeneous in K. WecanthendefineV t Vt K t which solves V t =max µ tp t P k x t (x t + δ t ) γ 2 P t k x 2 +(1+x) E t [Λ t+1 V t+1 ], where x t It K t δ t is the net investment rate. The first order condition for the net investment rate is x t = 1 γ (Q t 1), (1) 7

8 where Q t E t [Λ t+1 V t+1 ] P k t = E t [Λ t+1 V t+1 ] Pt kk. (2) t+1 Q is the ex-dividend market value of the firm divided by the replacement cost of its capital stock and γ controls adjustment costs. To build our empirical measures, we define Q = V e +(L FA) Inventories P k K where V e is the market value of equity, L are the liabilities (mostly measured at book values, but this is a rather small adjustment, see Hall [2001]), and FA are financial assets. Notice that the BEA measure of K now includes intangible assets (including software, R&D, and some intellectual property). As a result, our measure of Q is lower than in the previous literature. Because financial assets and liabilities contain large residuals, we also compute another measure of Q: Q misc = Q + Amisc L misc P k K where A misc and L misc are the miscellaneous assets and liabilities recorded in the financialaccounts. Since A misc >L misc,itfollowsthatq misc >Q. It is unclear which measure is more appropriate. Figure 3 shows the evolution of Q for the non financial corporate sector. according to both measures, by historical standards. As shown, Q is high Figure 3: Two Measures of Q Stock Q year Stock Q (misc) Non fin Corp Stock Q Non fin Corp Note: Annual data. Q for Non Financial Corporate sector (data fornoncorporatesectornotavailable) This leads us to our main conclusion: investment is low relative to Q. The top chart in Figure 4 shows the aggregate net investment rate for the non financial business sector along with the fitted 8

9 Figure 4: Net Investment vs. Q Net investment (actual and predicted with Q) NI/K year Net Investment Fitted values Prediction residuals (by period and cumulative) Regression residuals year Cumulative gap Residual Note: Annual data. Net investment for Non Financial Business sector. value for a regression on (lagged) Q from 1990 to The bottom chart shows the regression residuals (for each period and cumulative) from 1990 to Both charts clearly show that investment has been low relative to Q since sometime in the early 2000 s. 4 By 2015, the cumulative under-investment is more than 10% of capital. 5 4 By definition of OLS, the cumulative residual for 2002 is zero, buttheunderinvestmentfromthenonisstriking 5 Note that we focus on the past 25 years because measures of Q based on equity are not always stable and therefore do not fit long time series. This is a well known fact that might be due to long run changes in technology and/or participation in equity markets that make it difficult to compare the 2000 s with the 1960 s. Even in shorter windows, van Binsbergen and Opp [2016] argue convincingly that asset pricing anomalies that affect Q can have material consequences for real investment particularly for high Q firms. Q is therefore not a perfect benchmark, but it enables us to control for a wide range of factors and provides theoretical support for testing the remaining hypotheses. 9

10 The above regression focuses on aggregate investment. To study under-investment at a more granular level, we estimate panel regressions of industry- and firm-level investment on Q; andstudy the time effects. The details of the regression are discussed in Section Figure5 shows the results: time effects for the industry regression are shown ontheleftandforthefirmregressionon he right. The vertical line highlights the average time effect acrossallyearsforeachregression 6.As shown, the time-effects are substantially lower for both Industry- and Firm-level regressions from 2000 onward. In the industry regression, time effects were above average in most years from 1980 to 2000 but have been consistently below-average since. In the firm regression, time effects were fairly high in the 1980s and slightly high in the 1990s. They approach the average as early as 1999 and turn substantially negative thereafter. These results are robust to including additional measures of fundamentals such as cash flow; considering only asubsetofindustries;andeven splitting tangible and intangible assets (see Figure 17). These results are consistent with those in Alexander and Eberly [2016], who consider firm-level gross investment, defined as the ratio of capital expenditures to assets. We conclude that investment hasbeenlowrelativetoq since the early 2000 s. 6 Note that the time effects need not be zero, on average, given the impact of adjustment costs in Q theory and the inclusion of a constant in the regression. 10

11 Figure 5: Time effects from Industry and Firm-level regressions Industry level time effects (BEA) year=1981 year=1982 year=1983 year=1984 year=1985 year=1986 year=1987 year=1988 year=1989 year=1990 year=1991 year=1992 year=1993 year=1994 year=1995 year=1996 year=1997 year=1998 year=1999 year=2000 year=2001 year=2002 year=2003 year=2004 year=2005 year=2006 year=2007 year=2008 year=2009 year=2010 year=2011 year=2012 year=2013 year=2014 year= year=1981 year=1982 year=1983 year=1984 year=1985 year=1986 year=1987 year=1988 year=1989 year=1990 year=1991 year=1992 year=1993 year=1994 year=1995 year=1996 year=1997 year=1998 year=1999 year=2000 year=2001 year=2002 year=2003 year=2004 year=2005 year=2006 year=2007 year=2008 year=2009 year=2010 year=2011 year=2012 year=2013 year=2014 year=2015 Firm level time effects (Compustat) Note: Time fixed effects from industry- and firm-panel regressions of net investment on Q, withtimeaswellas industry/firm fixed effects. Industry investment data from BEA; firm investment based on CAPX/Assets from Compustat. 1.3 Fact 3: Following a Secular Increase, Depreciation Has Remained Stable Since 2000 The decrease in net investment could be the result of changes in the depreciation rate. To test this, Figure 6 shows the gross investment rate, the net investment rate and the depreciation rate for the non financial corporate sector on the top, and the non financial non corporate sector on the bottom. Note that these series include residential structures, but their contribution is relatively small for non financial businesses. The gross investment rate isdefinedastheratioof Grossfixed capital formation with equity REITs to lagged capital. Depreciation rates are defined as the ratio of consumption of fixed capital, equipment, software, and structures, including equity REIT to lagged capital; and net investment rates as the gross investment rate minus the depreciation rate. In the non corporate sector, depreciation is stable and net investment follows gross investment. The evolution is more complex in the corporate sector. There was a secular increase in depreciation from 1960 until 2000, driven primarily by a shift in the composition of corporate investment (from structures and equipment to intangibles). As a result, the trend in net investment is significantly lower than the trend in gross investment from 1960 to Since 2000, however, the share of 11

12 Figure 6: Investment and Depreciation Rate for Non financial Business Sector Non Financial Corporate year Net I/K Depreciation/K Gross I/K Non Financial Non Corporate year Net I/K Depreciation/K Gross I/K Note: Annual data. Non financial corporate sector on the top, non financial non corporate sector on the bottom. intangible assets has remained flat such that depreciation has been more stable, and, if anything, it has decreased. The drop in net investment over the past 15 years is therefore due to a drop in gross investment, not a rise in depreciation. Because the corporate sectorcontributesthelionshareof investment, the aggregate figure for the combined non-financial sector resembles the top panel (see Table 1). 1.4 Fact 4: Firm Entry has Decreased Figure 7 shows two measures of firm entry: the establishment entry and exit rates as reported by the U.S. Census Bureau s Business Dynamics Statistics (BDS); and the average number of firms by 12

13 industry in Compustat. In the early 1990s, we see a large increase in firms in Compustat, driven primarily by firms going public. Since then, both charts provide strong evidence of a decline in the number of firms. This downward trend in business dynamism has been highlighted by numerous papers (e.g., Decker et al. [2014]) but the trend has been particularly severe in recent years. Infact, Decker et al. [2015] arguethat, whereasinthe1980sand1990sdecliningdynamism was observed in selected sectors (notably retail), the decline was observed acrossallsectorsinthe2000s,including the traditionally high-growth information technology sector. Figure 7: Firm entry, exit and number of firms Establishment entry and exit rates (Census) year Entry rate (Census) Exit rate (Census) Average number of firms by industry (Compustat) year Note: Annual data. The Compustat and Census patterns above appear quite different. However, focusing on the post-2000 period (the main period of interest) and the sectors for which Compustat provides good 13

14 coverage, we find significant similarities. Figure 8 shows the 3-year log change in the number of firms based on Compustat and the number of establishments based on Census BDS data (excluding agriculture and construction for which Compustat provides limited coverage). As shown, changes in the number of firms are roughly similar across all sectors, including manufacturing, mining and retail which are the main contributors of investment. Figure 8: Comparison of 3-Year log change in # of establishments (Census) and firms (Compustat), by SIC sector Mining Manufacturing TCU Wholesale Retail Services year Census (left) Compustat (right) Note: Annual data. Agriculture and construction omitted because Compustat provides limited coverage for these sectors 1.5 Fact 5: Institutional Ownership and Payouts Have Increased The top graph of Figure 9 shows the total buybacks and payouts for all US-incorporated firmsin Compustat. As shown, there has been a substantial increase in totalpayouts, primarilydrivenby an increase in share buybacks. The increase starts soon after 1982,whenSECRule10b-18was instituted (noted by the vertical line). Rule 10b-18 allows companies to repurchase their shares on the open market without regulatory limits. The bottom graph shows the average share of institutional ownership, by type. Again, we see a substantial increase in institutional ownership particularly since The increase is primarily driven by growth in transient and quasi-indexer institutions. This is not shown in the figure, but the increase is particularly pronounced for smaller firms: since 2000, thedollar-weightedshareofquasi- indexer institutional ownership increased from ~30% to ~45%, while the median share increased 14

15 from ~15% to ~50%. That is, while the dollar-weighted quasi-indexer ownership increased by about 50%, it more than doubled for the median firm. These two effects are remarkable, and closely match the timing ofdecreasinginvestmentsatthe aggregate level. Figure 9: Payouts and Institutional ownership Share Buybacks and Payouts year Payouts/Assets Buybacks/Assets Average share of institutional ownership, by type year All institutions Dedicated Quasi Indexer Transient Notes: Annual data for all US incorporated firms in Compustat. Results are similar when including foreignincorporated firms. The vertical line in the first graph highlights the passing of SEC rule 10b-18, which allows companies to repurchase their shares on the open market without regulatory limits. 15

16 2 What might explain the under-investment? The basic Q-equation (1) says that Q should be a sufficient statistic for investment, while equation (2) equates Q with the average market to book value. This theory is based on the following assumptions [Hayashi, 1982]: no financial constraints; shareholder value maximization; constant returns to scale and perfect competition; Low investment despite high levels of Q might be explained by a variety of theories we consider the following eight (grouped into four broad categories) 7 : Financial frictions 1. External finance constraints: Alargeliteraturehasarguedthatfrictionsinfinancial markets can constrain investment decisions and force firms to rely on internal funds.see Fazzari et al. [1987], Gomes [2001], Moyen [2004], and Hennessy and Whited [2007]. 8 Similarly, Rajan and Zingales [1998] showthatindustrialsectorsthatarerelativelymore in need of external financing develop disproportionately faster in countries with more developed financial markets. Thus, if certain sectors depend on external finance to invest and are unable to obtain the required funds, they may under-invest relative to Q. 2. Bank dependence: Financial constraints may differ between bank-dependent firms and firms with access to the capital markets. As a result, we also test whether bank dependent firms are responsible for the under-investment (see, for instance, Alfaro et al. [2015]). This hypothesis is supported by recent papers such as Chen et al. [2016], which shows that reductions in small business lending has affected investment by smaller firms Safe asset scarcity: Safe asset scarcity and/or changes in the composition of assets may affect corporations capital costs (see Caballero and Farhi [2014], for example). In their simple form, such variations would impact Q. They would not cause a gap between Q and investment. However, a gap may appear if safe firms are unable or unwilling to 7 We also considered changes in R&D expenses as a proxy for lack of ideas (i.e., differences between average and marginal Q). Firms increasing R&D expenses are likely to have better ideas and therefore a higher marginal Q. So we test whether under-investing industries (and firms) exhibit a parallel decrease in R&D expense. We do not find support for this hypothesis, but this is inconclusive: under some theories, a rise in R&D may actually imply lower marginal Q (e.g., if ideas are harder to identify). We were unable to find abettermeasurefor(lackof)ideas,sowe cannot rule out this hypothesis. 8 There is considerable controversy about the implications of financialfrictions,ofcourse,butthisdoesnotmatter for our analysis because we are not interested in estimating elasticities. While financial frictions make internal funds relevant, it is not at all clear that they increase the sensitivity of investment to cash flows. [Kaplan and Zingales, 1997] and Gomes [2001] show that financial frictions might not decrease the fit of the Q equation much. 9 We should say from the outset that our ability to test this hypothesis is rather limited. Our industry data includes all firms, but investment is skewed and tends to be dominated by relatively large firms. Our firm-level data does not cover small firms. 16

17 take full advantage of low funding cost (due to, for example, product market rents). See section for additional discussion and results relevant to this hypothesis. Measurement Error 4. Intangibles: The rise of intangibles may affect investment in several ways: first, intangible investment is difficult to measure and is therefore prone to measurement error. Under-estimation of I would lead to under-estimation of K, andthereforeover-estimation of Q; andwouldtranslatetoan observed under-investmentatindustries with a higher share of intangibles. Alternatively, intangible assets might be more difficult to accumulate. A rise in the relative importance of intangibles could then lead to a higher equilibrium value of Q even if intangibles are correctly measured Globalization: officialgdpstatisticsonprivateinvestmentaimtocaptureinvestment that occurs physically in the US, regardless of where the firm making the investment is incorporated. For example, the investment series would include a manufacturing plant in Michigan built by a German company and exclude investment in China by a US Retail company. Thus, we may observe lower US private investment if US firms with foreign activities are investing more abroad, or foreign firms are investing less in the US. This would be pure measurement error: consolidated investment at thefirm-levelwouldstill follow Q, butwouldnotbeincludedinusfinancialaccounts. Competition 6. Regulations & uncertainty: Regulation and regulatory uncertainty may affect investment in two ways. First, increased regulation may stifle competition by raising barriers to entry. Second, per the theory of investment under uncertainty, irreversible investment in an industry may decline if economic agents are uncertain about future payoffs (see, for example, Bernanke [1983]). Thus, increased regulation and the associated regulatory uncertainty may restrain investment Concentration: Alargeliteraturehasstudiedthelinkbetweencompetition,investment, and innovation (see Aghion et al. [2014] foradiscussion). Fromatheoretical perspective, we know that the relationship is non-monotonic becauseofatrade-offbetween average and marginal profits. For a large set of parameters, however, we can 10 Intangibles can also interact with information technology and competition. For instance, Amazon does not need to open new stores to serve new customers; it simply needs to expand its distribution network. This may lead to a lower equilibrium level of tangible capital (e.g., structures and equipment), thus a lower investment level on tangible assets. But this would still be consistent with Q theory since the Q of incumbent would fall. On the other hand, Amazon should increase its investments in intangible assets. Whether the Q of Amazon remains large then depends mostly on competition. Finally, intangible assets can be used as a barrier to entry. For all these reasons, we think that it is important to consider intangible investment together with competition. 11 Increases in firm-specific uncertainty may also lead to lower investment levels due to manager risk-aversion Panousi and Papanikolauo [2012] and/or irreversible investment[pindyck, 1988, Dixit and Pindyck, 1994]. We test this hypothesis using stock market return and sales volatility and find some, albeit limited support for this hypothesis. 17

18 expect competition to increase innovation and investment. Firms in concentrated industries, aging industries and/or incumbents that do not face the threat of entry might have weak incentives to invest. 12 This hypothesis is supported by a growing literature that argues that competition may be decreasing in several economic sectors (see for example CEA [2016], Decker et al. [2015], Mongey [2016]). Similarly, Jovanovic and Rousseau [2014] highlightsdifferencesinthesensitivityofinvestmenttoq between incumbents and new entrants. Blonigen and Pierce [2016] studiestheimpactofmergersandacquisitions (M&As) on productivity and market power, and finds that M&As are associated with increases in average markups. Given the rise in M&A over the past decades, this suggests apotentialriseinmarketpower. Governance 8. Firm Ownership: ownership can affect management incentives through governance and investment horizon. Regarding short-termism, some have argued that equity markets can put excessive emphasis on quarterly earnings, and that higher stock-based compensation incentivizes managers to focus on short term share prices rather than long term profits (see Martin [2015], Lazonick [2014], for example). Given the rise of institutional ownership, an increase in market-induced short-termism may lead firms to cut their long term investment expenditures. On the other hand, ownership may improve governance. A large literature following Jensen [1986] arguesthatconflictsofinterestbetweenmanagersandshareholders can lead firms to invest in ways that do not maximize shareholder value. 13 Harford et al. [2008] andrichardson [2006] findevidencethatpoorgovernanceassociateswithgreater industry-adjusted investment. Thus, improvements in governance driven by changes in ownership may lead to lower investment levels. Several recent papers study the implications of shareholder activismandinstitutional ownership on governance and payouts. Appel et al. [2016a] findthatpassiveownersin- fluence firms governance choices (they lead to more independent directors, lower takeover defenses, and more equal voting rights; as well as more votes against management). 14 Appel et al. [2016b] findthatlargerownershipstakesofpassiveinstitutionalinvestors make firms more susceptible to activist investors (increasing the ambitiousness of activist objectives as well as the rate of success); and Crane et al. [2016] showthathigher(total and quasi-indexer) institutional ownership causes firms to increase their payouts. Together, this growing literature suggests that increases in passive institutional ownership 12 We do not take a stand on the drivers of increased concentration; simply that it appears in the data. 13 This does not necessarily imply that managers invest too much; they might invest in the wrong projects instead. The general view, however, is that managers are reluctant to return cash to shareholders, and that they might over-invest. 14 Schmidt and Fahlenbrach [2016] findoppositeeffectsforsomegovernancemeasures(including the likelihood of CEO becoming chairman and appointment of new independent directors), though they focus on a smaller sample. They also find that higher passive ownership leads to more value-destructing M&A. 18

19 lead to tighter governance. However, it is unclear whether the increased susceptibility to activist investors and higher payouts are in fact evidence oftightergovernanceor increased short-termism. Some papers provide qualitative support for governance (e.g., Crane et al. [2016] refertochang et al. [2014] whicharguesthatincreasingpassiveinstitutional ownership leads to share price increases), but it is inconclusive. And other studies such as Asker et al. [2014] showthatpublicfirmsinvestsubstantiallylessand are less responsive to changes in investment opportunities than private firms. In the end, although these two hypotheses impact investment through very different mechanisms, 15 differentiating between them is quite challenging. Improvements in governance reduce managerial entrenchment and require managers to continuously demonstrate strong performance, just as increased short-termism would. We are therefore unable to differentiate between these two hypotheses. We simply test whether increases in (passive) institutional ownership lead to higher payouts andlowerinvestment. 3 Data Testing the above theories requires the use of micro data. We gather and analyze a wide range of aggregate-, industry- and firm-level data. The data fields and data sources are summarized in Table 2. Sections3.1 and 3.2 discuss the aggregate and industry datasets, respectively. Section 3.3 discusses the firm-level investment and Q datasets; and 3.4 discusses all other data sources, including the explanatory variables used to test each theory. We discuss data reconciliation and data validation results where appropriate. 3.1 Aggregate data Aggregate data on funding costs, profitability, investment and market value for the US Economy and the non financial sector is gathered from the US Flow of Funds accounts through FRED. These data are used in the aggregate analyses discussed in Section 1; intheconstructionofaggregateq; and to reconcile and ensure the accuracy of more granular data. In addition, data on aggregate firm entry and exit is gathered from the Census BDS; and used in the aggregate regressions reported in Section Industry investment data Dataset Industry-level investment and profitability data including measures of private fixed assets (currentcost and chained values for the net stock of capital, depreciation and investment) and value added 15 Improved governance aligns the (manager s) maximization problem with that of the shareholder s, thereby increasing the focus on long term value. Increased short-termism shifts the objective function of the maximization towards short-term value. 19

20 Table 2: Data sources Primary datasets Additional datasets Data fields Source Granularity Aggregate investment and Q Flow of Funds US Industry-level investment and BEA ~NAICS L3 operating surplus Firm-level financials Compustat Firm Sales and Market Value Census NAICS L3 Concentration Entry/Exit; firm demographics Census SIC L2 Occupational Licensing PDII Survey NAICS L3 Regulation index Mercatus NAICS L3 Industry-level spreads Egon Zakrajsek NAICS L3 Institutional ownership Thomson Reuters 13F Firm Bushee s institutional investor Brian Bushee s website Institutional classification Investor (gross operating surplus, compensation and taxes) are gathered from the Bureau of Economic Analysis (BEA). Fixed assets data is available in three categories: structures, equipment and intellectual property (which includes includes software, R&D and expenditures forentertainment, literary, andartistic originals, among others). This breakdown allows us to (i) study investment patterns for intellectual property separate from the more traditional definitions of K (structures and equipment); and (ii) better capture total investment in aggregate regressions, as opposed to only capital expenditures. Investment and profitability data are available at the sector (15groups)anddetailedindustry (63 groups) level, in a similar categorization as the 2007 NAICS Level 3. We start with the 63 detailed industries and group them into 39 industry groupings that contribute a material share of investment (see Appendix I: Industry Investment Data for details on the investment dataset). We exclude Financials and Real Estate; and also exclude Utilities given the influence of government actions in their investment and their unique experience after the crisis (e.g., they exhibit decreasing operating surplus since 2000). This leaves 36 industry groupings for our analyses, whose total net investment since 2000 is summarized in Table 14 in the appendix. All other datasets are mapped into these 36 industry groupings using the NAICS Level 3 mapping provided by the BEA. We define industry-level gross investment rates as the ratio of Investment in Private Fixed Assets to lagged Current-Cost Net Stock of Private Fixed Assets ; depreciation rates as the ratio of Current-Cost Depreciation of Private Fixed Assets to lagged Current-Cost Net Stock of Private Fixed Assets ; and net investment rates as the gross investment rate minus the depreciation rate. Investment rates are computed across all asset types, as well asseparatingintellectualproperty from structures and equipment. The Gross Operating Surplus is provided by the BEA, while the Net Operating Surplus is computed as the Gross Operating Surplus minus Current-Cost Depreciation of Private Fixed 20

21 Assets. OS/K is defined as the Net Operating Surplus over the lagged Current-Cost Net Stock of Private Fixed Assets Data validation In order to ensure industry-level figures are consistent with aggregatedata,wereconcilethetwo datasets. We first note that industry-level figures include all forms of organization (financials and non financials, as well as corporates, non corporates and non businesses). A breakdown between financials and non financials or corporates and non corporates byindustryisnotavailable. Thus, afullreconciliationcanonlybeachievedattheaggregatelevel or considering pre-aggregated BEA series (such as non financial corporates). But these do not provide an industry breakdown. Instead, we note that aggregating capital, depreciation and operating surplus across all industries except Financials and Real Estate yields very similar series as the aggregated non financial business series sourced from the Flow of Funds (see Figure 10). The remaining differences appear to be explained by non-businesses (households and non profit organizations) butcannotbereconciledduetodata availability. Regardless, the trends are sufficiently similar to suggest that conclusions based on industry data will be consistent with the aggregate-level under-investment discussed in Section 1. Figure 10: Reconciliation of Flow of Funds and BEA industry datasets Notes: Flow of Funds data for non financial business sector; BEA data for all industries except Finance and Real Estate. Remaining differences particularly for OS/K appear to be driven by non-businesses(households and non profit), which are included in the BEA series but not in the Flow offundsseries. 3.3 Firm-level investment and Q data Dataset Firm-level data is primarily sourced from Compustat, which includes all public firms in the US. Data is available from 1950 through 2016, but coverage is fairly thin until the 1970s. We exclude firm-year observations with assets under $1 million; with negative book or market value; or with missing year, assets, Q, orbookliabilities. 16 In order to more closely mirror the aggregate and industry figures, we exclude utilities (SIC codes 4900 through 4999), real estate (SIC codes These exclusion rules are applied for all measures except firm age,whichstartsonthefirstyearinwhichthe firm appears in Compustat irrespective of data coverage 21

22 through 5399) and financial firms (SIC codes 6000 through 6999); and focus on US incorporated firms (see Section for additional discussion). Firms are mapped to BEA industry segments using Level 3 NAICScodes, asdefinedbythe BEA. When NAICS codes are not available, firms are mapped to the most common NAICS category among those firms that share the same SIC code and have NAICS codes available. Firms with an other SIC code (SIC codes 9000 to 9999) are excluded from industry-level analyses because they cannot be mapped to an industry. Firm-level data is used for two purposes: first, we aggregate firm-level data into industry-level metrics and use the aggregated quantities to explain industry-level investment behavior (e.g., by computing industry-level Q). We consider the aggregate (i.e., weighted average), the mean and the median for all quantities, and use the specification that exhibits the highest statistical significance. Second, we use firm-level data to analyze the determinants of firm-level investment through panel regressions (see Section 4 for additional details) Data validation The sample of Compustat firms that we study represents a wide cross-section of firms in the US. Still, this set of firms may not be representative of aggregate andindustry-levelinvestment figures. For instance, Compustat captures investment by US public firms, while official GDP statistics capture all investment that occurs physically in the US irrespective of the listing status or country of the firm making the investment. To address this issue, Figure 11 plots the gross fixed capital formation for non financial businesses (from the Flow of Funds) versus total capital expenditures (CAPX) for two sets of Compustat firms: all firms in Compustat, irrespective of country of incorporation, and all domestically incorporated firms. Simply summing up CAPX for all firms results in aseriesthatroughlytracks, andsometimesexceeds, theofficial Flow of Funds estimates. However, this Compustat series exhibits a much stronger recovery after the Dotcom bubble and the Great Recession than the official estimates: total CAPX accounts for 85%ofinvestmentfrom1980to 2000, on average; but 117% from 2008 to Focusing on US incorporated firms largely resolves the differences: the new series accounts for 63% of investment from1980to2000and59% from 2008 to 2015, on average. These results suggest that Foreign-incorporated firms are investing more than US-incorporated firms, but this investment is occurring outsidetheus. In order to more closely mirror US aggregate figures, we restrict our sample to US incorporated firms for the remainder of our analyses. None of the qualitative conclusions in this paper are sensitive to the inclusion of all firms irrespective of country of incorporation. We are interested in using Compustat firm-level data to reach conclusions about industry-level investment. Thus, we need to understand whether Compustat firms in a given industry provide a good representation of the industry as a whole. We define the following two measures of coverage : the ratio of Compustat total CAPX to BEA Investment by industry, and the ratio of Compustat total PP&E to BEA Capital. Table 14 in the Appendix shows the coverage for the 36 industries under consideration. As shown, our Compustat sample provides good coverage for the majority of 22